Detection of Cardiovascular Disease Using Ensemble Feature Engineering With Decision Tree

Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which becoming leading cause human death across the globe. Hence, an essential framework is demanded for early detection CVDs can help to prevent premature death. The application Artificial Intelligence (AI) in healthcare has opted this challenge and makes it easier detect using computational model. In study, authors built reduced dataset ensemble feature selection methods got five features as per their weight values. Support Vector Machine, Logistic Regression, Decision Tree classification techniques utilized check effectiveness newly designed datasets through different validation approaches. also worked on data processing visualization techniques, Principal Component Analysis (PCA), T-sne understanding structure. From findings, was possible conclude that DT achieved optimal accuracy AUC 98.9% 0.99 ROC with leave one out Cross Validation (CV).